Metabolomics Data Processing and Data Analysis-Current Best Practices

• Outros Autores: Hanhineva, Kati (Editor), Van der Hooft, Justin (Editor)
• Formato: Recurso Eletrônico Capítulo de Livro
• Idioma: inglês
• Publicado em: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Assuntos: Research & information: general; metabolic networks; mass spectral libraries; metabolite annotation; metabolomics data mapping; nontarget analysis; liquid chromatography mass spectrometry; compound identification; tandem mass spectral library; forensics; wastewater; gut microbiome; meta-omics; metagenomics; metabolomics; metabolic reconstructions; genome-scale metabolic modeling; constraint-based modeling; flux balance; host-microbiome; metabolism; global metabolomics; LC-MS; spectra processing; pathway analysis; enrichment analysis; mass spectrometry; liquid chromatography; MS spectral prediction; metabolite identification; structure-based chemical classification; rule-based fragmentation; combinatorial fragmentation; time series; PLS; NPLS; variable selection; bootstrapped-VIP; data repository; computational metabolomics; reanalysis; lipidomics; data processing; triplot; multivariate risk modeling; environmental factors; disease risk; chemical classification; in silico workflows; metabolome mining; molecular families; networking; substructures; mass spectrometry imaging; metabolomics imaging; biostatistics; ion selection algorithms; liquid chromatography high-resolution mass spectrometry; data-independent acquisition; all ion fragmentation; targeted analysis; untargeted analysis; R programming; full-scan MS/MS processing; R-MetaboList 2; liquid chromatography-mass spectrometry (LC/MS); fragmentation (MS/MS); data-dependent acquisition (DDA); simulator; in silico; untargeted metabolomics; liquid chromatography-mass spectrometry (LC-MS); experimental design; sample preparation; univariate and multivariate statistics; metabolic pathway and network analysis; metabolic profiling; computational statistical; unsupervised learning; supervised learning
• Acesso em linha: DOAB: download the publication; DOAB: description of the publication

Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme "Best Practices i...